Abstract

Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

title = "3D reconstruction using single-photon Lidar data exploiting the widths of the returns",

abstract = "Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.",

N2 - Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

AB - Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.